Access to life-saving humanitarian medicine in austere and resource-constrained environments is crucial to ensuring the health and safety of many around the world. However, access to high-quality medical care in these conditions is challenging due to the absence of connectivity and power, limited equipment, and the lack of available medical expertise. To address these constraints, we propose a memory-augmented Vision-Language Model (VLM) for action recognition and action anticipation to assist in medical decision-making and provide real-time intraoperative guidance to first responders and medical professionals performing life-saving interventions. To enhance the accuracy of predictions on the action recognition and action anticipation tasks, our proposed architecture consists of two modules: the vision module and the language module. The vision module is based on the MViTv2 transformer, and its outputs are fed into the language module. Because medical actions are often consecutive, with one action consistently performed after another, we augment the language module with memory of previous actions through concatenating all past actions recognized or anticipated by the vision module. This string is then passed into the language module, based upon the FlanT5 Large Language Model (LLM) architecture, which makes the final prediction on the recognized or anticipated action. The open-source Trauma THOMPSON dataset, which consists of unscripted egocentric videos of emergency trauma surgery procedures, is employed for model training and validation. Our proposed architecture achieves a top1 accuracy of 69.11% on action recognition and 62.13% on action anticipation, outperforming the results reported by previous literature.